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Record W4391353970 · doi:10.1016/j.eats.2024.102917

Arthroscopic Treatment of Cam‐Type Impingement for Femoroacetabular Impingement Using Patient's Own 1:1 Three‐Dimensional Printed Hip Model Without the Use of Fluoroscopy

2024· article· en· W4391353970 on OpenAlexaff
Ryland Murphy, Ivan Wong

Bibliographic record

VenueArthroscopy Techniques · 2024
Typearticle
Languageen
FieldMedicine
TopicHip disorders and treatments
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineFemoroacetabular impingementFluoroscopyHip arthroscopySurgeryHip painRadiologyArthroscopy

Abstract

fetched live from OpenAlex

The arthroscopic treatment of femoroacetabular impingement (FAI) has increased greatly in popularity over the past decades. Treatment involves the resection of abnormal bony morphology of the femoral head/neck (cam-type) and the acetabulum (pincer-type), which otherwise create damage from the pathologic contact between the 2 structures. More recently, in evaluating the postoperative success of FAI surgery, unsuccessful resection of the cam impingement has been identified as a leading cause for revision. To evaluate adequate cam resection intraoperatively, C-arm fluoroscopy is most commonly used. However, fluoroscopy has disadvantages, including its limited availability in smaller surgical centers, radiation exposure, and it only provides 2-dimensional information of a 3-dimensional problem. With the recent implementation of ultrasound-guided portal placement, a technique for adequate cam resection is the last barrier to eliminating the need for intraoperative imaging for FAI. We present a technique that uses a 1:1 3-dimensional printed model made from computed tomography scans that have the patient's unique anatomy, to better identify and quantify the resection of cam-type impingements. This technique is reproducible and can lead to better understanding of the cam resection for each individual patient. Further, when combined with ultrasound-guided portal placement, it eliminates the need for intraoperative fluoroscopy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.095
GPT teacher head0.361
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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